EPL-PRM: Equipotential line sampling strategy for probabilistic roadmap planners in narrow passages

Abstract

Path planning is a crucial concern in the field of mobile robotics, particularly in complex scenarios featuring narrow passages. Sampling-based planners, such as the widely utilized probabilistic roadmap (PRM), have been extensively employed in various robot applications. However, PRM’s utilization of random node sampling often results in disconnected graphs, posing a significant challenge when dealing with narrow passages. In order to tackle this issue, we present equipotential line sampling strategy for probabilistic roadmap (EPL-PRM), a novel approach derived from PRM. This paper initially proposes a sampling potential field, followed by the construction of equipotential lines that are denser in the proximity of obstacles and narrow passages. Random sampling is subsequently conducted along these lines. Consequently, the sampling strategy enhances the likelihood of sampling nodes around obstacles and narrow passages, thereby addressing the issue of sparsity encountered in traditional sampling-based planners. Furthermore, we introduce a nodal optimization method based on an artificial repulsive field, which prompts sampled nodes to move in the direction of repulsion. As a result, nodes around obstacles are distributed more uniformly, while nodes within narrow passages gravitate toward the middle of the passages. Finally, extensive simulations are conducted to evaluate the proposed method. The results demonstrate that our approach achieves path planning with superior efficiency, lower cost, and higher reliability compared with traditional algorithms.